Application of supergeneralized matched filters to target classification

The matched filter (MF) is the optimum linear operator for distinguishing between a fixed signal and noise, given the noise statistics. A generalized matched filter (GMF) is a linear filter that can handle the more difficult problem of a multiple-example signal set, and it reduces to a MF when the signal set has only one member. A supergeneralized matched filter (SGMF) is a set of GMFs and a procedure to combine their results nonlinearly to handle the multisignal problem even better. Obviously the SGMF contains the GMF as a special case. An algorithm for training SGMFs is presented, and it is shown that the algorithm performs quite well even for extremely difficult classification problems. (C) 2005 Optical Society of America.